{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "# Ignore numpy dtype warnings. These warnings are caused by an interaction\n",
    "# between numpy and Cython and can be safely ignored.\n",
    "# Reference: https://stackoverflow.com/a/40846742\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.ufunc size changed\")\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import nbinteract as nbi\n",
    "\n",
    "sns.set()\n",
    "sns.set_context('talk')\n",
    "np.set_printoptions(threshold=20, precision=2, suppress=True)\n",
    "pd.options.display.max_rows = 7\n",
    "pd.options.display.max_columns = 8\n",
    "pd.set_option('precision', 2)\n",
    "# This option stops scientific notation for pandas\n",
    "# pd.set_option('display.float_format', '{:.2f}'.format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-28T19:49:23.510266Z",
     "start_time": "2018-07-28T19:49:23.502170Z"
    },
    "code_folding": [],
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "palette = sns.color_palette('Set2')\n",
    "\n",
    "def plot_decision_boundary(X, y, theta):\n",
    "    \"\"\"\n",
    "    Plot X1, X2 for y=0 and y=1.\n",
    "    Then plot a decision boundary between y=0 and y=1.\n",
    "    \"\"\"\n",
    "    plt.scatter(X[y==0, 0], X[y==0, 1], color=palette[0], alpha=0.7)\n",
    "    plt.scatter(X[y==1, 0], X[y==1, 1], color=palette[1], alpha=0.7)\n",
    "    \n",
    "    z = np.zeros([100, 100])\n",
    "    uu = np.linspace(-3, 7, 100)\n",
    "    vv = np.linspace(-3, 7, 100)\n",
    "    for i, u in enumerate(uu):\n",
    "        for j, v in enumerate(vv):\n",
    "            z[i, j] = (pf.transform([[u, v]]) @ theta.T).item()\n",
    "    plt.contour(uu, vv, z.T, [0])\n",
    "    \n",
    "    plt.xlabel('$X_1$')\n",
    "    plt.ylabel('$X_2$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (outline, delete before merging)\n",
    "\n",
    "- Loss function for logistic regression:\n",
    "$$\n",
    "L(\\theta, X, y) = \\frac{1}{n} \\sum_{i} \\left(-y_i \\ln \\left(f_{\\hat\\theta} \\left(X_i\\right) \\right) - \\left(1 - y_i \\right) \\ln \\left(1 - f_{\\hat\\theta} (X_i)\\right)\\right)\n",
    "$$\n",
    "- Loss functions for regularized logistic regression (L1 and L2):\n",
    "$$\n",
    "L(\\theta, X, y) = \\frac{1}{n} \\sum_{i} \\left(-y_i \\ln \\left(f_{\\hat\\theta} \\left(X_i\\right) \\right) - \\left(1 - y_i \\right) \\ln \\left(1 - f_{\\hat\\theta} (X_i)\\right)\\right) + \\lambda\\sum_{i}\\left|\\theta_i\\right|\n",
    "$$\n",
    "$$\n",
    "L(\\theta, X, y) = \\frac{1}{n} \\sum_{i} \\left(-y_i \\ln \\left(f_{\\hat\\theta} \\left(X_i\\right) \\right) - \\left(1 - y_i \\right) \\ln \\left(1 - f_{\\hat\\theta} (X_i)\\right)\\right) + \\lambda\\sum_{i}{\\theta_i}^2\n",
    "$$\n",
    "- Why do we use regularization?\n",
    "    - To avoid overfitting \n",
    "    - When data are linearly separable, parameters will not converge\n",
    "- Example inspired by [https://gist.github.com/kevindavenport/c524268ed0713371aa32#file-regularized_logistic_regression_intuition-ipynb](https://gist.github.com/kevindavenport/c524268ed0713371aa32#file-regularized_logistic_regression_intuition-ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Regularization in Classification\n",
    "\n",
    "An easy way to visualize the importance of regularization in classification is to plot a decision boundary. Decision boundaries show the hyperplanes at which the classifier partitions observations. The classifier predicts all points on each side of the hyperplane to be of the same class.\n",
    "\n",
    "In the example below, we use a toy dataset of $X_1$ and $X_2$ values; each point's label is depicted by its color. We attempt to fit a fifth degree polynomial `X_feats` as the feature set for Logistic Regression, and we begin by not specifying any regularization parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-28T19:49:23.535782Z",
     "start_time": "2018-07-28T19:49:23.512845Z"
    },
    "code_folding": [],
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "np.random.seed(4)\n",
    "\n",
    "# x1, x2 values for y=0\n",
    "x1_0 = np.random.randn(100)\n",
    "x2_0 = np.random.randn(100) + 2.3\n",
    "y_0 = np.zeros(100)\n",
    "\n",
    "# x1, x2 values for y=1\n",
    "x1_1 = np.random.randn(100) + 2.2\n",
    "x2_1 = np.random.randn(100)\n",
    "y_1 = np.ones(100)\n",
    "\n",
    "# Concatenate to create train set\n",
    "X1 = np.c_[x1_0, x1_1]\n",
    "X2 = np.c_[x2_0, x2_1]\n",
    "X = np.r_[X1, X2]\n",
    "y = np.hstack([y_0, y_1])\n",
    "\n",
    "# Create polynomial features\n",
    "pf = PolynomialFeatures(degree=5, interaction_only=False, include_bias=True)\n",
    "X_feats = pf.fit_transform(X)\n",
    "\n",
    "# Create a test set\n",
    "x1_0_test = np.random.randn(74)\n",
    "x2_0_test = np.random.randn(74) + 2.3\n",
    "y_0_test = np.zeros(74)\n",
    "\n",
    "x1_1_test = np.random.randn(74) + 2.2\n",
    "x2_1_test = np.random.randn(74)\n",
    "y_1_test = np.ones(74)\n",
    "\n",
    "X1_test = np.c_[x1_0_test, x1_1_test]\n",
    "X2_test = np.c_[x2_0_test, x2_1_test]\n",
    "X_test = np.r_[X1_test, X2_test]\n",
    "X_test_feats = pf.transform(X_test)\n",
    "y_test = np.hstack([y_0_test, y_1_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-28T19:49:27.737395Z",
     "start_time": "2018-07-28T19:49:23.538724Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logreg = LogisticRegression(random_state=42)\n",
    "logreg.fit(X_feats, y)\n",
    "\n",
    "plot_decision_boundary(X, y, logreg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-28T19:49:27.806790Z",
     "start_time": "2018-07-28T19:49:27.751229Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9594594594594594"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, logreg.predict(X_test_feats))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the model is too complex for the dataset, the decision boundary overfits to the training data in order to attain a higher accuracy. We can attempt to mitigate this issue by adding an L2 regularization penalty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-28T19:49:31.355319Z",
     "start_time": "2018-07-28T19:49:27.812276Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logreg_l2 = LogisticRegression(penalty='l2', C=0.01, random_state=42)\n",
    "logreg_l2.fit(X_feats, y)\n",
    "\n",
    "plot_decision_boundary(X, y, logreg_l2.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By adding the L2 regularization penalty, we have found a model that does a better job at separating the two classes while maintaining its ability to generalize to unseen data. This is reflected in the higher accuracy score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-28T19:49:31.372142Z",
     "start_time": "2018-07-28T19:49:31.358226Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9797297297297297"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, logreg_l2.predict(X_test_feats))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we set an L1 regularization penalty, we can build a sparse model that sets feature weights to 0, resulting in an even simpler model that extracts the pattern from the noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-28T19:49:35.005297Z",
     "start_time": "2018-07-28T19:49:31.375707Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logreg_l1 = LogisticRegression(penalty='l1', C=0.01, random_state=42)\n",
    "logreg_l1.fit(X_feats, y)\n",
    "\n",
    "plot_decision_boundary(X, y, logreg_l1.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-28T19:49:35.013945Z",
     "start_time": "2018-07-28T19:49:35.007992Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9864864864864865"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, logreg_l1.predict(X_test_feats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
